A DATA QUALITY MEASUREMENT INFORMATION MODEL BASED ON ISO/IEC 15939 (Research-in-Progress) Ismael Caballero, Eugenio Verbo Department of Research & Development (Indra Software Factory, S.L.U.) Indra-UCLM Research and Development Institute Ronda de Toledo s/n – 13003 Ciudad Real, Spain {icaballerom, emverbo}@indra.es Coral Calero, Mario Piattini Department of Information Technologies and Systems (UCLM) Indra-UCLM Research and Development Institute Paseo de la Universidad 4 – 13071 Ciudad Real, Spain {Coral.Calero, Mario.Piattini}@uclm.es Abstract: Measurement is a key activity in DQ Management. Through DQ literature, one can discover a lot of proposals contributing somehow to the measurement of DQ issues. Looking at those proposals, it can be found out that there is a lack of unification of the nomenclature: different authors call to the same concepts in different way, or even, they do not explicitly recognize some of them. This may cause a misunderstanding of the proposed measures. The main aim of this paper is to propose a Data Quality Measurement Information Model (DQMIM) which provides a standardization of the referred terms by following ISO/IEC 15939 as a basis. This paper deals about the concepts implied in the measurement process, not about the measures themselves. In order to make operative the DQMIM, we have also designed a XML Schema which can be used to outline Data Quality Measurement Plans. Key Words: Data Quality Measurement, ISO/IEC 15939, Data Quality Measurement Information Model 1. INTRODUCTION Typically, an organization realizes about their data quality (hereafter DQ) problems when they have just affected negatively to the business performance. Once this has occurred, executives would quantify the impact of these DQ problems [17] to several levels (organizational, economic, customer satisfaction, or employee satisfaction) in order to be able to classify them and to outline DQ improvement plans. Any DQ improvement plan must begin with the assessment of the affected scenarios to identify the common roots of the detected problems. The assessment involves having values for DQ measures. The main intention of these measures is to provide a quantitative meaning about how much data quality dimensions are achieved in order to enable an adequate management [10]. Although DQ literature counts with a great amount of measurement proposals, it has still a lot of open researching challenges [3]. We think that one of these challenges consists of the unification of the different terms provided by different authors for the same concepts. In order to achieve this goal, an international standard about measuring could be a good starting point. ISO/IEC 15939 [16] has been selected for this proposal due to the similar characteristics that software and data share. The standard defines a Measurement Information Model (MIM), which is the basis for the Data Quality Measurement Information Model (DQMIM), described through section 2. Any data quality model composed, even a future standard containing the most comprehensive and universal applicable set of data quality dimensions, could be used together DQMIM, since it simply proposes a way to name the concepts participating in the measurement of the dimensions. Please, note that the aim of this paper is not to develop data quality measures, but providing a common nomenclature from measurement concepts to make easier the process of defining them. Measuring data quality depends on the view of a person playing a role and judging data from the point of Proceedings of the Twelfth International Conference on Information Quality (ICIQ-07) Page 393 of 576